Partition of Interval-Valued Observations Using Regression

Fei Liu () and L. Billard ()
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Fei Liu: Bank of America
L. Billard: University of Georgia,

Journal of Classification, 2022, vol. 39, issue 1, No 5, 55-77

Abstract: Abstract Both regression modeling and clustering methodologies have been extensively studied as separate techniques. There has been some activity in using regression-based algorithms to partition a data set into clusters for classical data; we propose one such algorithm to cluster interval-valued data. The new algorithm is based on the k-means algorithm of MacQueen (1967) and the dynamical partitioning method of Diday and Simon (1976), with the partitioning criteria being based on establishing regression models for each sub-cluster. This also depends on distance measures between the underlying regression models for each sub-cluster. Several types of simulated data sets are generated for several different data structures. The proposed k-regressions algorithm consistently out-performs the k-means algorithm. Elbow plots are used to identify the total number of clusters K in the partition. The new method is also applied to real data.

Keywords: Clusters; k-means algorithm; k-regressions algorithm; Hausdorff distance; City-block distance; Center distance; Simulation methods; Real-data application (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00357-021-09394-5

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